Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations1605
Missing cells2671
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory275.9 KiB
Average record size in memory176.0 B

Variable types

Numeric19
Text2

Alerts

bod_max is highly overall correlated with bod_min and 3 other fieldsHigh correlation
bod_min is highly overall correlated with bod_maxHigh correlation
conductivity_max is highly overall correlated with bod_max and 1 other fieldsHigh correlation
conductivity_min is highly overall correlated with bod_max and 1 other fieldsHigh correlation
do_max is highly overall correlated with do_minHigh correlation
do_min is highly overall correlated with bod_max and 1 other fieldsHigh correlation
fecal_coliform_max is highly overall correlated with fecal_coliform_min and 4 other fieldsHigh correlation
fecal_coliform_min is highly overall correlated with fecal_coliform_max and 4 other fieldsHigh correlation
fecal_streptococci_max is highly overall correlated with fecal_coliform_max and 4 other fieldsHigh correlation
fecal_streptococci_min is highly overall correlated with fecal_coliform_max and 3 other fieldsHigh correlation
nitrate_max is highly overall correlated with nitrate_minHigh correlation
nitrate_min is highly overall correlated with nitrate_maxHigh correlation
ph_max is highly overall correlated with ph_minHigh correlation
ph_min is highly overall correlated with ph_maxHigh correlation
total_coliform_max is highly overall correlated with fecal_coliform_max and 3 other fieldsHigh correlation
total_coliform_min is highly overall correlated with fecal_coliform_max and 4 other fieldsHigh correlation
state_name has 124 (7.7%) missing values Missing
nitrate_min has 56 (3.5%) missing values Missing
nitrate_max has 56 (3.5%) missing values Missing
fecal_coliform_min has 184 (11.5%) missing values Missing
fecal_coliform_max has 185 (11.5%) missing values Missing
total_coliform_min has 224 (14.0%) missing values Missing
total_coliform_max has 224 (14.0%) missing values Missing
fecal_streptococci_min has 766 (47.7%) missing values Missing
fecal_streptococci_max has 767 (47.8%) missing values Missing
temp_min is highly skewed (γ1 = 31.20516608) Skewed
conductivity_min is highly skewed (γ1 = 34.51328681) Skewed
bod_min is highly skewed (γ1 = 29.27994682) Skewed
bod_max is highly skewed (γ1 = 27.00620253) Skewed
nitrate_min is highly skewed (γ1 = 27.50700342) Skewed
nitrate_max is highly skewed (γ1 = 35.10507529) Skewed
fecal_coliform_min is highly skewed (γ1 = 34.37295455) Skewed
fecal_coliform_max is highly skewed (γ1 = 21.59338436) Skewed
total_coliform_min is highly skewed (γ1 = 33.49430653) Skewed
total_coliform_max is highly skewed (γ1 = 24.02009987) Skewed
fecal_streptococci_max is highly skewed (γ1 = 24.80671161) Skewed
bod_min has 56 (3.5%) zeros Zeros
bod_max has 52 (3.2%) zeros Zeros
nitrate_min has 170 (10.6%) zeros Zeros
nitrate_max has 122 (7.6%) zeros Zeros

Reproduction

Analysis started2025-08-31 04:45:50.735319
Analysis finished2025-08-31 04:46:21.641173
Duration30.91 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

station_code
Real number (ℝ)

Distinct1604
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4516.0486
Minimum1
Maximum30089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:21.696438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1062.2
Q11819
median2951
Q34415
95-th percentile10154.8
Maximum30089
Range30088
Interquartile range (IQR)2596

Descriptive statistics

Standard deviation5948.38
Coefficient of variation (CV)1.3171647
Kurtosis12.368924
Mean4516.0486
Median Absolute Deviation (MAD)1348
Skewness3.5466915
Sum7248258
Variance35383224
MonotonicityNot monotonic
2025-08-31T10:16:21.943220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4118 2
 
0.1%
1276 1
 
0.1%
3887 1
 
0.1%
2406 1
 
0.1%
2404 1
 
0.1%
1274 1
 
0.1%
1272 1
 
0.1%
2405 1
 
0.1%
1271 1
 
0.1%
1270 1
 
0.1%
Other values (1594) 1594
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
4 1
0.1%
5 1
0.1%
7 1
0.1%
9 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
ValueCountFrequency (%)
30089 1
0.1%
30088 1
0.1%
30087 1
0.1%
30086 1
0.1%
30085 1
0.1%
30084 1
0.1%
30083 1
0.1%
30082 1
0.1%
30081 1
0.1%
30080 1
0.1%
Distinct1598
Distinct (%)99.9%
Missing6
Missing (%)0.4%
Memory size25.1 KiB
2025-08-31T10:16:22.160113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length163
Median length114
Mean length48.145716
Min length8

Characters and Unicode

Total characters76985
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1597 ?
Unique (%)99.9%

Sample

1st rowmg/L mg/L Ml 100 mL
2nd rowRIVER BEAS AT U/S MANALI
3rd rowRIVER BEAS AT D/S MANALI
4th rowRIVER BEAS D/S OF WASTE PROCESSING FACILITY AT MANALI
5th rowRIVER BEAS D/S MANALSU NALLAH
ValueCountFrequency (%)
river 1636
 
13.8%
at 1278
 
10.8%
d/s 309
 
2.6%
of 274
 
2.3%
near 259
 
2.2%
bridge 237
 
2.0%
u/s 213
 
1.8%
village 147
 
1.2%
ganga 112
 
0.9%
district 109
 
0.9%
Other values (2908) 7261
61.4%
2025-08-31T10:16:22.469049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 10828
14.1%
10236
13.3%
R 7509
 
9.8%
I 5252
 
6.8%
E 4528
 
5.9%
T 3782
 
4.9%
N 3664
 
4.8%
D 2650
 
3.4%
H 2630
 
3.4%
S 2434
 
3.2%
Other values (53) 23472
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 10828
14.1%
10236
13.3%
R 7509
 
9.8%
I 5252
 
6.8%
E 4528
 
5.9%
T 3782
 
4.9%
N 3664
 
4.8%
D 2650
 
3.4%
H 2630
 
3.4%
S 2434
 
3.2%
Other values (53) 23472
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 10828
14.1%
10236
13.3%
R 7509
 
9.8%
I 5252
 
6.8%
E 4528
 
5.9%
T 3782
 
4.9%
N 3664
 
4.8%
D 2650
 
3.4%
H 2630
 
3.4%
S 2434
 
3.2%
Other values (53) 23472
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 10828
14.1%
10236
13.3%
R 7509
 
9.8%
I 5252
 
6.8%
E 4528
 
5.9%
T 3782
 
4.9%
N 3664
 
4.8%
D 2650
 
3.4%
H 2630
 
3.4%
S 2434
 
3.2%
Other values (53) 23472
30.5%

state_name
Text

Missing 

Distinct116
Distinct (%)7.8%
Missing124
Missing (%)7.7%
Memory size25.1 KiB
2025-08-31T10:16:22.585766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length106
Median length41
Mean length11.548953
Min length3

Characters and Unicode

Total characters17104
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)5.3%

Sample

1st rowHIMACHAL PRADESH
2nd rowHIMACHAL PRADESH
3rd rowHIMACHAL PRADESH
4th rowHIMACHAL PRADESH
5th rowHIMACHAL PRADESH
ValueCountFrequency (%)
pradesh 438
18.2%
madhya 152
 
6.3%
himachal 143
 
5.9%
odisha 125
 
5.2%
maharashtra 118
 
4.9%
uttar 113
 
4.7%
karnataka 110
 
4.6%
bihar 98
 
4.1%
assam 92
 
3.8%
jharkhand 60
 
2.5%
Other values (133) 964
40.0%
2025-08-31T10:16:22.789995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3787
22.1%
H 1760
10.3%
R 1429
 
8.4%
S 1103
 
6.4%
D 993
 
5.8%
932
 
5.4%
T 893
 
5.2%
M 867
 
5.1%
I 702
 
4.1%
E 674
 
3.9%
Other values (23) 3964
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3787
22.1%
H 1760
10.3%
R 1429
 
8.4%
S 1103
 
6.4%
D 993
 
5.8%
932
 
5.4%
T 893
 
5.2%
M 867
 
5.1%
I 702
 
4.1%
E 674
 
3.9%
Other values (23) 3964
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3787
22.1%
H 1760
10.3%
R 1429
 
8.4%
S 1103
 
6.4%
D 993
 
5.8%
932
 
5.4%
T 893
 
5.2%
M 867
 
5.1%
I 702
 
4.1%
E 674
 
3.9%
Other values (23) 3964
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3787
22.1%
H 1760
10.3%
R 1429
 
8.4%
S 1103
 
6.4%
D 993
 
5.8%
932
 
5.4%
T 893
 
5.2%
M 867
 
5.1%
I 702
 
4.1%
E 674
 
3.9%
Other values (23) 3964
23.2%

temp_min
Real number (ℝ)

Skewed 

Distinct149
Distinct (%)9.3%
Missing7
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean21.876471
Minimum0.3
Maximum3836
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:22.862650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile6.185
Q115
median19
Q322
95-th percentile27
Maximum3836
Range3835.7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation109.11229
Coefficient of variation (CV)4.9876553
Kurtosis1020.5604
Mean21.876471
Median Absolute Deviation (MAD)4
Skewness31.205166
Sum34958.6
Variance11905.493
MonotonicityNot monotonic
2025-08-31T10:16:22.952775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 111
 
6.9%
22 102
 
6.4%
19 101
 
6.3%
18 97
 
6.0%
16 90
 
5.6%
21 81
 
5.0%
15 75
 
4.7%
24 72
 
4.5%
23 68
 
4.2%
25 65
 
4.0%
Other values (139) 736
45.9%
ValueCountFrequency (%)
0.3 6
0.4%
0.9 3
0.2%
1.1 1
 
0.1%
1.2 1
 
0.1%
1.4 1
 
0.1%
2 5
0.3%
2.4 1
 
0.1%
2.6 4
0.2%
2.9 1
 
0.1%
3 7
0.4%
ValueCountFrequency (%)
3836 1
 
0.1%
2115 1
 
0.1%
100 1
 
0.1%
29 8
 
0.5%
28.9 2
 
0.1%
28.7 1
 
0.1%
28 20
1.2%
27.5 4
 
0.2%
27.4 1
 
0.1%
27 45
2.8%

temp_max
Real number (ℝ)

Distinct164
Distinct (%)10.3%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean27.23325
Minimum1.1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:23.040051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile13.5
Q124
median29
Q331.1
95-th percentile35
Maximum39
Range37.9
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation6.2328132
Coefficient of variation (CV)0.22886777
Kurtosis2.0490941
Mean27.23325
Median Absolute Deviation (MAD)3
Skewness-1.2591552
Sum43491.5
Variance38.84796
MonotonicityNot monotonic
2025-08-31T10:16:23.128739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 140
 
8.7%
28 126
 
7.9%
32 125
 
7.8%
29 108
 
6.7%
31 106
 
6.6%
33 72
 
4.5%
24 65
 
4.0%
27 64
 
4.0%
22 63
 
3.9%
26 63
 
3.9%
Other values (154) 665
41.4%
ValueCountFrequency (%)
1.1 1
0.1%
1.8 1
0.1%
2.4 1
0.1%
2.5 2
0.1%
2.8 1
0.1%
3 1
0.1%
4 1
0.1%
4.1 2
0.1%
4.4 1
0.1%
4.7 1
0.1%
ValueCountFrequency (%)
39 4
 
0.2%
38 7
 
0.4%
37.9 1
 
0.1%
37 17
1.1%
36.8 2
 
0.1%
36.6 1
 
0.1%
36.4 1
 
0.1%
36 36
2.2%
35.5 2
 
0.1%
35 29
1.8%

do_min
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)6.3%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean5.9132123
Minimum0.3
Maximum28.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:23.212451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.98
Q15.2
median6.3
Q37.1
95-th percentile8.2
Maximum28.2
Range27.9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation2.1143517
Coefficient of variation (CV)0.35756398
Kurtosis14.490061
Mean5.9132123
Median Absolute Deviation (MAD)0.9
Skewness0.52050182
Sum9443.4
Variance4.4704832
MonotonicityNot monotonic
2025-08-31T10:16:23.294166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 82
 
5.1%
0.3 72
 
4.5%
7.2 68
 
4.2%
7.1 58
 
3.6%
6 56
 
3.5%
6.8 49
 
3.1%
6.2 49
 
3.1%
6.6 47
 
2.9%
6.7 46
 
2.9%
5 46
 
2.9%
Other values (91) 1024
63.8%
ValueCountFrequency (%)
0.3 72
4.5%
0.4 3
 
0.2%
0.5 1
 
0.1%
0.6 2
 
0.1%
0.7 1
 
0.1%
0.9 1
 
0.1%
1 4
 
0.2%
1.1 4
 
0.2%
1.2 2
 
0.1%
1.4 3
 
0.2%
ValueCountFrequency (%)
28.2 1
 
0.1%
25 1
 
0.1%
22 1
 
0.1%
11.4 1
 
0.1%
11.2 3
0.2%
11 1
 
0.1%
10.8 1
 
0.1%
10.2 1
 
0.1%
10 3
0.2%
9.8 2
0.1%

do_max
Real number (ℝ)

High correlation 

Distinct115
Distinct (%)7.2%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean7.9230432
Minimum0.3
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:23.373637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile4.98
Q17.1
median7.9
Q39
95-th percentile10.8
Maximum28
Range27.7
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.9151637
Coefficient of variation (CV)0.24172072
Kurtosis10.087097
Mean7.9230432
Median Absolute Deviation (MAD)1
Skewness-0.088591418
Sum12653.1
Variance3.6678521
MonotonicityNot monotonic
2025-08-31T10:16:23.455451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 86
 
5.4%
7.6 67
 
4.2%
9.2 50
 
3.1%
8.2 50
 
3.1%
7.9 49
 
3.1%
8.4 47
 
2.9%
8 46
 
2.9%
8.9 44
 
2.7%
7.5 44
 
2.7%
6.2 43
 
2.7%
Other values (105) 1071
66.7%
ValueCountFrequency (%)
0.3 14
0.9%
0.8 1
 
0.1%
1 2
 
0.1%
1.1 1
 
0.1%
1.2 2
 
0.1%
1.4 1
 
0.1%
1.5 3
 
0.2%
1.7 1
 
0.1%
1.9 2
 
0.1%
2.1 2
 
0.1%
ValueCountFrequency (%)
28 1
 
0.1%
14.1 1
 
0.1%
13.7 3
0.2%
13.5 1
 
0.1%
13.2 1
 
0.1%
13 1
 
0.1%
12.7 1
 
0.1%
12.6 1
 
0.1%
12.5 1
 
0.1%
12.4 2
0.1%

ph_min
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)4.8%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean12.81742
Minimum1
Maximum755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:23.535785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.5
Q17
median7.2
Q37.5
95-th percentile8
Maximum755
Range754
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation43.804484
Coefficient of variation (CV)3.4175741
Kurtosis97.896736
Mean12.81742
Median Absolute Deviation (MAD)0.3
Skewness9.1230345
Sum20469.42
Variance1918.8328
MonotonicityNot monotonic
2025-08-31T10:16:23.619783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 180
11.2%
7.3 142
 
8.8%
7.1 141
 
8.8%
7.4 133
 
8.3%
7 118
 
7.4%
7.5 112
 
7.0%
6.8 96
 
6.0%
7.6 85
 
5.3%
6.9 83
 
5.2%
7.7 75
 
4.7%
Other values (67) 432
26.9%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
2.6 1
 
0.1%
2.7 3
0.2%
2.8 1
 
0.1%
3.1 1
 
0.1%
3.4 1
 
0.1%
3.6 1
 
0.1%
3.7 1
 
0.1%
3.8 3
0.2%
ValueCountFrequency (%)
755 1
0.1%
517 1
0.1%
410 1
0.1%
404 1
0.1%
403 1
0.1%
389 1
0.1%
364 1
0.1%
348 2
0.1%
346 1
0.1%
336 1
0.1%

ph_max
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)4.9%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean20.992473
Minimum3
Maximum1878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:23.702520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7.3
Q17.9
median8.2
Q38.4
95-th percentile8.8
Maximum1878
Range1875
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation104.19924
Coefficient of variation (CV)4.9636476
Kurtosis114.3272
Mean20.992473
Median Absolute Deviation (MAD)0.3
Skewness9.8103028
Sum33524.98
Variance10857.482
MonotonicityNot monotonic
2025-08-31T10:16:23.785278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.2 174
10.8%
8.4 158
9.8%
8.3 152
 
9.5%
8.5 142
 
8.8%
8.1 136
 
8.5%
8 121
 
7.5%
7.9 119
 
7.4%
7.8 96
 
6.0%
7.6 63
 
3.9%
7.5 54
 
3.4%
Other values (68) 382
23.8%
ValueCountFrequency (%)
3 3
0.2%
3.2 1
 
0.1%
3.5 1
 
0.1%
4.1 1
 
0.1%
4.3 1
 
0.1%
4.5 3
0.2%
4.7 1
 
0.1%
4.9 1
 
0.1%
5.4 1
 
0.1%
5.5 1
 
0.1%
ValueCountFrequency (%)
1878 1
0.1%
1265 1
0.1%
1080 1
0.1%
989 1
0.1%
954 1
0.1%
953 1
0.1%
864 1
0.1%
861 1
0.1%
808 1
0.1%
807 1
0.1%

conductivity_min
Real number (ℝ)

High correlation  Skewed 

Distinct569
Distinct (%)35.6%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean282.43945
Minimum0
Maximum34400
Zeros7
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:23.867982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.44
Q1115
median198
Q3315
95-th percentile761.2
Maximum34400
Range34400
Interquartile range (IQR)200

Descriptive statistics

Standard deviation898.77712
Coefficient of variation (CV)3.182194
Kurtosis1303.908
Mean282.43945
Median Absolute Deviation (MAD)93
Skewness34.513287
Sum451055.8
Variance807800.32
MonotonicityNot monotonic
2025-08-31T10:16:23.953487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 21
 
1.3%
120 14
 
0.9%
140 13
 
0.8%
190 12
 
0.7%
102 12
 
0.7%
110 11
 
0.7%
128 11
 
0.7%
126 11
 
0.7%
210 11
 
0.7%
152 10
 
0.6%
Other values (559) 1471
91.7%
ValueCountFrequency (%)
0 7
 
0.4%
1 21
1.3%
1.1 7
 
0.4%
1.2 9
0.6%
1.3 2
 
0.1%
1.4 2
 
0.1%
1.5 3
 
0.2%
1.7 1
 
0.1%
1.8 3
 
0.2%
1.9 1
 
0.1%
ValueCountFrequency (%)
34400 1
0.1%
5908 1
0.1%
2612 1
0.1%
1769 1
0.1%
1656 1
0.1%
1567 1
0.1%
1434 1
0.1%
1413 1
0.1%
1384 1
0.1%
1360 1
0.1%

conductivity_max
Real number (ℝ)

High correlation 

Distinct940
Distinct (%)58.9%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1138.2594
Minimum0
Maximum54200
Zeros7
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:24.039424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.8
Q1245
median440
Q3768
95-th percentile2525.6
Maximum54200
Range54200
Interquartile range (IQR)523

Descriptive statistics

Standard deviation4001.248
Coefficient of variation (CV)3.5152338
Kurtosis91.968671
Mean1138.2594
Median Absolute Deviation (MAD)239
Skewness9.0976796
Sum1817800.3
Variance16009985
MonotonicityNot monotonic
2025-08-31T10:16:24.290447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
264 8
 
0.5%
280 8
 
0.5%
518 8
 
0.5%
240 8
 
0.5%
2.6 7
 
0.4%
0 7
 
0.4%
190 7
 
0.4%
176 7
 
0.4%
178 7
 
0.4%
716 6
 
0.4%
Other values (930) 1524
95.0%
(Missing) 8
 
0.5%
ValueCountFrequency (%)
0 7
0.4%
1 5
0.3%
1.5 1
 
0.1%
1.6 2
 
0.1%
1.8 1
 
0.1%
1.9 1
 
0.1%
2 1
 
0.1%
2.3 2
 
0.1%
2.4 4
0.2%
2.5 5
0.3%
ValueCountFrequency (%)
54200 1
0.1%
53650 1
0.1%
44100 1
0.1%
42600 1
0.1%
42210 1
0.1%
41900 1
0.1%
40570 1
0.1%
37450 1
0.1%
36540 1
0.1%
36200 1
0.1%

bod_min
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct108
Distinct (%)6.8%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean197.5817
Minimum0
Maximum170000
Zeros56
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:24.370736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1.1
Q32
95-th percentile6.22
Maximum170000
Range170000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5214.2161
Coefficient of variation (CV)26.390178
Kurtosis879.59653
Mean197.5817
Median Absolute Deviation (MAD)0.1
Skewness29.279947
Sum315537.97
Variance27188050
MonotonicityNot monotonic
2025-08-31T10:16:24.455888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 620
38.6%
1.1 110
 
6.9%
2 102
 
6.4%
1.2 89
 
5.5%
3 58
 
3.6%
0 56
 
3.5%
1.8 46
 
2.9%
1.4 43
 
2.7%
1.3 43
 
2.7%
2.1 35
 
2.2%
Other values (98) 395
24.6%
ValueCountFrequency (%)
0 56
3.5%
0.3 7
 
0.4%
0.32 1
 
0.1%
0.33 1
 
0.1%
0.52 1
 
0.1%
0.55 1
 
0.1%
0.58 1
 
0.1%
0.61 1
 
0.1%
0.62 2
 
0.1%
0.66 1
 
0.1%
ValueCountFrequency (%)
170000 1
0.1%
120000 1
0.1%
9300 1
0.1%
6100 1
0.1%
5500 1
0.1%
572 1
0.1%
306 1
0.1%
200 1
0.1%
180 1
0.1%
48 1
0.1%

bod_max
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct172
Distinct (%)10.8%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean674.1144
Minimum0
Maximum470000
Zeros52
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:24.540615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.7
median2.6
Q33.8
95-th percentile21
Maximum470000
Range470000
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation15473.463
Coefficient of variation (CV)22.953764
Kurtosis757.96073
Mean674.1144
Median Absolute Deviation (MAD)1
Skewness27.006203
Sum1076560.7
Variance2.3942805 × 108
MonotonicityNot monotonic
2025-08-31T10:16:24.624702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 158
 
9.8%
2 70
 
4.4%
2.8 69
 
4.3%
3 69
 
4.3%
2.5 64
 
4.0%
2.6 64
 
4.0%
2.4 60
 
3.7%
0 52
 
3.2%
1.9 49
 
3.1%
2.9 47
 
2.9%
Other values (162) 895
55.8%
ValueCountFrequency (%)
0 52
 
3.2%
0.3 5
 
0.3%
0.32 1
 
0.1%
0.62 1
 
0.1%
0.75 1
 
0.1%
0.78 1
 
0.1%
0.88 1
 
0.1%
0.89 1
 
0.1%
1 158
9.8%
1.1 19
 
1.2%
ValueCountFrequency (%)
470000 1
0.1%
380000 1
0.1%
100000 1
0.1%
81000 1
0.1%
31000 1
0.1%
5500 1
0.1%
560 1
0.1%
294 1
0.1%
145 1
0.1%
131 1
0.1%

nitrate_min
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct216
Distinct (%)13.9%
Missing56
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean373.63478
Minimum0
Maximum230000
Zeros170
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:24.707092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3
median0.32
Q30.62
95-th percentile2.632
Maximum230000
Range230000
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation6779.7129
Coefficient of variation (CV)18.145294
Kurtosis873.63978
Mean373.63478
Median Absolute Deviation (MAD)0.11
Skewness27.507003
Sum578760.28
Variance45964507
MonotonicityNot monotonic
2025-08-31T10:16:24.790879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 389
24.2%
0.3 279
17.4%
0 170
 
10.6%
0.02 42
 
2.6%
0.6 39
 
2.4%
0.7 32
 
2.0%
2 26
 
1.6%
0.4 22
 
1.4%
0.5 17
 
1.1%
0.33 16
 
1.0%
Other values (206) 517
32.2%
(Missing) 56
 
3.5%
ValueCountFrequency (%)
0 170
10.6%
0.02 42
 
2.6%
0.03 1
 
0.1%
0.04 1
 
0.1%
0.06 1
 
0.1%
0.1 1
 
0.1%
0.11 1
 
0.1%
0.12 3
 
0.2%
0.13 1
 
0.1%
0.14 3
 
0.2%
ValueCountFrequency (%)
230000 1
 
0.1%
70000 3
0.2%
45000 1
 
0.1%
33000 1
 
0.1%
17000 1
 
0.1%
13000 1
 
0.1%
11000 1
 
0.1%
7800 1
 
0.1%
2400 1
 
0.1%
2300 1
 
0.1%

nitrate_max
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct547
Distinct (%)35.3%
Missing56
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean13430.24
Minimum0
Maximum14000000
Zeros122
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:24.872501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.67
median1.43
Q33.38
95-th percentile27.248
Maximum14000000
Range14000000
Interquartile range (IQR)2.71

Descriptive statistics

Standard deviation371927.94
Coefficient of variation (CV)27.693321
Kurtosis1299.9152
Mean13430.24
Median Absolute Deviation (MAD)1.1
Skewness35.105075
Sum20803441
Variance1.383304 × 1011
MonotonicityNot monotonic
2025-08-31T10:16:24.957979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122
 
7.6%
0.3 93
 
5.8%
0.32 31
 
1.9%
1.4 21
 
1.3%
1.2 19
 
1.2%
1.3 14
 
0.9%
1.6 12
 
0.7%
1 12
 
0.7%
1.1 12
 
0.7%
1.42 11
 
0.7%
Other values (537) 1202
74.9%
(Missing) 56
 
3.5%
ValueCountFrequency (%)
0 122
7.6%
0.26 1
 
0.1%
0.3 93
5.8%
0.31 5
 
0.3%
0.32 31
 
1.9%
0.33 9
 
0.6%
0.34 6
 
0.4%
0.35 2
 
0.1%
0.36 4
 
0.2%
0.37 5
 
0.3%
ValueCountFrequency (%)
14000000 1
 
0.1%
3400000 1
 
0.1%
2600000 1
 
0.1%
170000 2
0.1%
130000 1
 
0.1%
94000 1
 
0.1%
78000 1
 
0.1%
49000 2
0.1%
17000 1
 
0.1%
4900 4
0.2%

fecal_coliform_min
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct186
Distinct (%)13.1%
Missing184
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean4027.5154
Minimum0
Maximum2200000
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:25.042554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median27
Q3350
95-th percentile6300
Maximum2200000
Range2200000
Interquartile range (IQR)347

Descriptive statistics

Standard deviation60217.317
Coefficient of variation (CV)14.95148
Kurtosis1248.3795
Mean4027.5154
Median Absolute Deviation (MAD)25
Skewness34.372955
Sum5723099.4
Variance3.6261252 × 109
MonotonicityNot monotonic
2025-08-31T10:16:25.127114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 353
22.0%
4 56
 
3.5%
130 34
 
2.1%
7 33
 
2.1%
1300 31
 
1.9%
110 29
 
1.8%
360 29
 
1.8%
6 28
 
1.7%
11 26
 
1.6%
78 23
 
1.4%
Other values (176) 779
48.5%
(Missing) 184
 
11.5%
ValueCountFrequency (%)
0 1
 
0.1%
1.42 1
 
0.1%
2 353
22.0%
3 10
 
0.6%
4 56
 
3.5%
5 8
 
0.5%
6 28
 
1.7%
7 33
 
2.1%
8 19
 
1.2%
9 19
 
1.2%
ValueCountFrequency (%)
2200000 1
 
0.1%
310000 1
 
0.1%
170000 2
0.1%
130000 3
0.2%
110000 4
0.2%
100000 2
0.1%
94000 1
 
0.1%
82000 1
 
0.1%
79000 1
 
0.1%
68000 1
 
0.1%

fecal_coliform_max
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct251
Distinct (%)17.7%
Missing185
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean79086.489
Minimum2
Maximum24000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:25.210365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q130
median210
Q32300
95-th percentile92000
Maximum24000000
Range23999998
Interquartile range (IQR)2270

Descriptive statistics

Standard deviation919614.45
Coefficient of variation (CV)11.627959
Kurtosis516.19575
Mean79086.489
Median Absolute Deviation (MAD)208
Skewness21.593384
Sum1.1230281 × 108
Variance8.4569073 × 1011
MonotonicityNot monotonic
2025-08-31T10:16:25.297518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 114
 
7.1%
92000 57
 
3.6%
6 35
 
2.2%
170 33
 
2.1%
1100 31
 
1.9%
4 27
 
1.7%
110 25
 
1.6%
2300 24
 
1.5%
920 24
 
1.5%
1600 23
 
1.4%
Other values (241) 1027
64.0%
(Missing) 185
 
11.5%
ValueCountFrequency (%)
2 114
7.1%
3 2
 
0.1%
4 27
 
1.7%
5 5
 
0.3%
6 35
 
2.2%
7 10
 
0.6%
8 15
 
0.9%
9 13
 
0.8%
10 12
 
0.7%
11 8
 
0.5%
ValueCountFrequency (%)
24000000 1
 
0.1%
21000000 1
 
0.1%
7000000 2
0.1%
4800000 1
 
0.1%
4100000 1
 
0.1%
4000000 1
 
0.1%
2700000 1
 
0.1%
2200000 2
0.1%
1700000 3
0.2%
1400000 1
 
0.1%

total_coliform_min
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct206
Distinct (%)14.9%
Missing224
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean6314.8479
Minimum2
Maximum3200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:25.376874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q133
median170
Q31100
95-th percentile11000
Maximum3200000
Range3199998
Interquartile range (IQR)1067

Descriptive statistics

Standard deviation89247.465
Coefficient of variation (CV)14.132956
Kurtosis1191.541
Mean6314.8479
Median Absolute Deviation (MAD)160
Skewness33.494307
Sum8720805
Variance7.9651101 × 109
MonotonicityNot monotonic
2025-08-31T10:16:25.462003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 76
 
4.7%
17 51
 
3.2%
110 43
 
2.7%
1100 36
 
2.2%
170 31
 
1.9%
350 31
 
1.9%
330 29
 
1.8%
21 27
 
1.7%
490 26
 
1.6%
3300 25
 
1.6%
Other values (196) 1006
62.7%
(Missing) 224
 
14.0%
ValueCountFrequency (%)
2 76
4.7%
3 1
 
0.1%
4 8
 
0.5%
5 4
 
0.2%
6 1
 
0.1%
7 4
 
0.2%
8 4
 
0.2%
9 5
 
0.3%
10 7
 
0.4%
11 12
 
0.7%
ValueCountFrequency (%)
3200000 1
 
0.1%
540000 1
 
0.1%
270000 1
 
0.1%
220000 3
0.2%
210000 2
0.1%
170000 1
 
0.1%
150000 1
 
0.1%
140000 2
0.1%
130000 1
 
0.1%
120000 1
 
0.1%

total_coliform_max
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct254
Distinct (%)18.4%
Missing224
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean360842.84
Minimum2
Maximum1.6 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:25.544841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile41
Q1170
median1300
Q34900
95-th percentile160000
Maximum1.6 × 108
Range1.6 × 108
Interquartile range (IQR)4730

Descriptive statistics

Standard deviation5346977.8
Coefficient of variation (CV)14.818024
Kurtosis647.27872
Mean360842.84
Median Absolute Deviation (MAD)1230
Skewness24.0201
Sum4.9832396 × 108
Variance2.8590171 × 1013
MonotonicityNot monotonic
2025-08-31T10:16:25.635016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1600 79
 
4.9%
160000 79
 
4.9%
350 49
 
3.1%
4900 42
 
2.6%
49 38
 
2.4%
280 35
 
2.2%
92000 29
 
1.8%
170 28
 
1.7%
920 26
 
1.6%
2000 25
 
1.6%
Other values (244) 951
59.3%
(Missing) 224
 
14.0%
ValueCountFrequency (%)
2 18
1.1%
4 5
 
0.3%
6 1
 
0.1%
16 1
 
0.1%
17 1
 
0.1%
20 2
 
0.1%
21 1
 
0.1%
22 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
ValueCountFrequency (%)
160000000 1
0.1%
92000000 1
0.1%
54000000 1
0.1%
35000000 1
0.1%
28000000 1
0.1%
14000000 1
0.1%
9400000 1
0.1%
9200000 1
0.1%
7900000 1
0.1%
6300000 1
0.1%

fecal_streptococci_min
Real number (ℝ)

High correlation  Missing 

Distinct75
Distinct (%)8.9%
Missing766
Missing (%)47.7%
Infinite0
Infinite (%)0.0%
Mean115.96377
Minimum1.8
Maximum17000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:25.720915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2
Q12
median2
Q317
95-th percentile240
Maximum17000
Range16998.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation772.46797
Coefficient of variation (CV)6.6612873
Kurtosis293.4359
Mean115.96377
Median Absolute Deviation (MAD)0
Skewness15.269753
Sum97293.6
Variance596706.76
MonotonicityNot monotonic
2025-08-31T10:16:25.804769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 518
32.3%
4 29
 
1.8%
17 24
 
1.5%
120 23
 
1.4%
210 18
 
1.1%
14 13
 
0.8%
240 12
 
0.7%
6 12
 
0.7%
110 11
 
0.7%
5 11
 
0.7%
Other values (65) 168
 
10.5%
(Missing) 766
47.7%
ValueCountFrequency (%)
1.8 1
 
0.1%
2 518
32.3%
2.8 1
 
0.1%
3 2
 
0.1%
4 29
 
1.8%
5 11
 
0.7%
6 12
 
0.7%
7 9
 
0.6%
8 4
 
0.2%
9 5
 
0.3%
ValueCountFrequency (%)
17000 1
0.1%
7900 1
0.1%
6300 1
0.1%
4900 1
0.1%
4600 1
0.1%
4100 1
0.1%
3400 1
0.1%
2700 1
0.1%
2400 1
0.1%
2000 1
0.1%

fecal_streptococci_max
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct108
Distinct (%)12.9%
Missing767
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean1375.8229
Minimum2
Maximum540000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2025-08-31T10:16:26.058756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median6
Q3220
95-th percentile1430
Maximum540000
Range539998
Interquartile range (IQR)218

Descriptive statistics

Standard deviation19792.548
Coefficient of variation (CV)14.385971
Kurtosis661.27308
Mean1375.8229
Median Absolute Deviation (MAD)4
Skewness24.806712
Sum1152939.6
Variance3.9174496 × 108
MonotonicityNot monotonic
2025-08-31T10:16:26.140539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 364
22.7%
290 32
 
2.0%
540 22
 
1.4%
4 21
 
1.3%
490 19
 
1.2%
240 18
 
1.1%
6 15
 
0.9%
17 14
 
0.9%
3 14
 
0.9%
20 12
 
0.7%
Other values (98) 307
19.1%
(Missing) 767
47.8%
ValueCountFrequency (%)
2 364
22.7%
2.6 1
 
0.1%
3 14
 
0.9%
4 21
 
1.3%
5 8
 
0.5%
6 15
 
0.9%
7 5
 
0.3%
8 3
 
0.2%
9 4
 
0.2%
11 2
 
0.1%
ValueCountFrequency (%)
540000 1
0.1%
130000 2
0.1%
35000 2
0.1%
17000 1
0.1%
16000 1
0.1%
13000 1
0.1%
11000 1
0.1%
9800 1
0.1%
9400 2
0.1%
9200 1
0.1%

Interactions

2025-08-31T10:16:19.706822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:53.398985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-31T10:15:56.281371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.928029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:59.406128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.685301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:02.163875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:03.574652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:05.152297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:06.469764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.834440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:09.531785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.803837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:12.372792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-31T10:16:20.327702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.068634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:55.566094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:56.959288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:58.723086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.011135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:01.338653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-31T10:16:04.283972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-31T10:16:07.126698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:08.736415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.125138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:11.469081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:12.993392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.271658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:15.593755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.067756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:18.504349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.394255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.144413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:55.642994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.035573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:58.791746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.083155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:01.413013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:02.868266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:04.358151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:05.810408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.200386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:08.840205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.190356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:11.544044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.061435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.337134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:15.814060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.139765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:18.619584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.460896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.210631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:55.712748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.108154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:58.855207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.145427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:01.480309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:02.935586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:04.426469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-31T10:16:08.923427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.252294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:11.616723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.128984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.406227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:15.883936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.212073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:18.767868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.525115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.280266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:55.785788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.174867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:58.920053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.208418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:01.691200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:03.006235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:04.498727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:05.951732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.334554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:09.005430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.314361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:11.689224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.194640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.468694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:15.953021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.287974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:18.908170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.595452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.352419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:55.862134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.251810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:58.988517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.275945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:01.760458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:03.077804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:04.571408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:06.030123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.405153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:09.087781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.380750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:11.765937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.263249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.540493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:16.021573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.361253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:19.035777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.667035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.419804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:55.937491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.339627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:59.055644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.338651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:01.826704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:03.155465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:04.641957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:06.101728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.472754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:09.165873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.443091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:11.839104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.327167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.604271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:16.085553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.430776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:19.319003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.735347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.491080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:56.003299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.452201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:59.121634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.405250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:01.893249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:03.233602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:04.711852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:06.177063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.544943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:09.241379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.508870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:11.911542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.391193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.675489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:16.152083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.501598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:19.400229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.803406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-08-31T10:15:56.072137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.587180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:59.190109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.472176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:01.957832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:03.321152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:04.941484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:06.250303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.615456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:09.317351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.584676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:11.984719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.457378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.742938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:16.218842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.575999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:19.475464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.878428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.640925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:56.142440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.722194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:59.264354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.545895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:02.028206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:03.409533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:05.012521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:06.328020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.690959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:09.393755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.666930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:12.221350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.527286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.813790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:16.292991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.663686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:19.555292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:20.952547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:54.715575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:56.212790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:57.830647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:15:59.335944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:00.615137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:02.099118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:03.498404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:05.085832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:06.400349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:07.764657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:09.465028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:10.737040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:12.299300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:13.600785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:14.886517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:16.360719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:17.746297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T10:16:19.634305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-31T10:16:26.216166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bod_maxbod_minconductivity_maxconductivity_mindo_maxdo_minfecal_coliform_maxfecal_coliform_minfecal_streptococci_maxfecal_streptococci_minnitrate_maxnitrate_minph_maxph_minstation_codetemp_maxtemp_mintotal_coliform_maxtotal_coliform_min
bod_max1.0000.6980.5370.544-0.390-0.5580.2780.2760.3440.3320.154-0.0130.0470.021-0.0600.2450.2600.2840.382
bod_min0.6981.0000.3650.422-0.404-0.4180.1500.2280.2800.3730.1290.043-0.153-0.063-0.1080.1410.2870.1630.345
conductivity_max0.5370.3651.0000.797-0.336-0.426-0.078-0.115-0.065-0.0560.2290.0020.2360.144-0.0640.2630.382-0.033-0.012
conductivity_min0.5440.4220.7971.000-0.355-0.402-0.060-0.0290.0180.0510.1680.0900.1920.246-0.0500.2520.377-0.0500.054
do_max-0.390-0.404-0.336-0.3551.0000.5620.1750.058-0.044-0.159-0.264-0.1220.1590.0420.066-0.100-0.3430.1930.034
do_min-0.558-0.418-0.426-0.4020.5621.000-0.216-0.217-0.326-0.364-0.178-0.0080.0890.1800.077-0.310-0.337-0.209-0.292
fecal_coliform_max0.2780.150-0.078-0.0600.175-0.2161.0000.8280.7500.524-0.160-0.106-0.018-0.0700.1080.324-0.1080.9210.793
fecal_coliform_min0.2760.228-0.115-0.0290.058-0.2170.8281.0000.7520.658-0.185-0.047-0.028-0.0400.0310.277-0.1060.7040.873
fecal_streptococci_max0.3440.280-0.0650.018-0.044-0.3260.7500.7521.0000.757-0.0900.036-0.134-0.2320.0020.4340.2080.6560.754
fecal_streptococci_min0.3320.373-0.0560.051-0.159-0.3640.5240.6580.7571.0000.0930.232-0.174-0.187-0.0290.2860.2220.4310.650
nitrate_max0.1540.1290.2290.168-0.264-0.178-0.160-0.185-0.0900.0931.0000.5800.122-0.009-0.027-0.1240.131-0.168-0.200
nitrate_min-0.0130.0430.0020.090-0.122-0.008-0.106-0.0470.0360.2320.5801.0000.0190.0360.032-0.1360.057-0.178-0.116
ph_max0.047-0.1530.2360.1920.1590.089-0.018-0.028-0.134-0.1740.1220.0191.0000.520-0.0570.0030.031-0.025-0.086
ph_min0.021-0.0630.1440.2460.0420.180-0.070-0.040-0.232-0.187-0.0090.0360.5201.0000.043-0.1020.002-0.104-0.105
station_code-0.060-0.108-0.064-0.0500.0660.0770.1080.0310.002-0.029-0.0270.032-0.0570.0431.000-0.106-0.0860.1050.018
temp_max0.2450.1410.2630.252-0.100-0.3100.3240.2770.4340.286-0.124-0.1360.003-0.102-0.1061.0000.4270.3210.325
temp_min0.2600.2870.3820.377-0.343-0.337-0.108-0.1060.2080.2220.1310.0570.0310.002-0.0860.4271.000-0.0640.020
total_coliform_max0.2840.163-0.033-0.0500.193-0.2090.9210.7040.6560.431-0.168-0.178-0.025-0.1040.1050.321-0.0641.0000.775
total_coliform_min0.3820.345-0.0120.0540.034-0.2920.7930.8730.7540.650-0.200-0.116-0.086-0.1050.0180.3250.0200.7751.000

Missing values

2025-08-31T10:16:21.070377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-31T10:16:21.198605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-31T10:16:21.400327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

station_codemonitoring_locationstate_nametemp_mintemp_maxdo_mindo_maxph_minph_maxconductivity_minconductivity_maxbod_minbod_maxnitrate_minnitrate_maxfecal_coliform_minfecal_coliform_maxtotal_coliform_mintotal_coliform_maxfecal_streptococci_minfecal_streptococci_max
01986mg/L mg/L Ml 100 mLNaN100.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11001RIVER BEAS AT U/S MANALIHIMACHAL PRADESH2.024.07.89.27.28.268.0380.01.02.80.321.152.0170.063.0540.02.02.0
22601RIVER BEAS AT D/S MANALIHIMACHAL PRADESH2.013.07.69.06.58.158.0135.01.02.80.321.87110.01600.0920.01600.02.02.0
34444RIVER BEAS D/S OF WASTE PROCESSING FACILITY AT MANALIHIMACHAL PRADESH2.013.07.88.86.77.862.0113.01.02.80.321.08110.01600.0350.01600.02.02.0
44037RIVER BEAS D/S MANALSU NALLAHHIMACHAL PRADESH2.014.07.98.96.38.052.0137.01.01.00.321.7422.0110.079.0540.02.02.0
53866RIVER BEAS U/S BEFORE CONF. OF MANALSU NALLAHHIMACHAL PRADESH2.013.07.89.17.07.851.0113.01.01.00.320.9723.0120.0110.0430.02.02.0
62602RIVER BEAS, U/S KULLUHIMACHAL PRADESH4.016.07.68.76.77.844.0128.01.01.00.320.9049.0220.0240.0920.02.02.0
74445RIVER BEAS D/S OF WASTE PROCESSING FACILITY AT KULLUHIMACHAL PRADESH4.016.07.68.46.67.773.0170.01.01.60.320.9647.0150.0280.0920.02.02.0
81002RIVER BEAS D/S KULLUHIMACHAL PRADESH4.016.07.58.47.28.077.0144.01.01.60.322.65110.0540.0540.01600.02.02.0
91003RIVER BEAS D/S AUTHIMACHAL PRADESH5.016.07.58.17.28.261.0129.01.01.00.320.7734.0280.0170.01600.02.02.0
station_codemonitoring_locationstate_nametemp_mintemp_maxdo_mindo_maxph_minph_maxconductivity_minconductivity_maxbod_minbod_maxnitrate_minnitrate_maxfecal_coliform_minfecal_coliform_maxtotal_coliform_mintotal_coliform_maxfecal_streptococci_minfecal_streptococci_max
160130068RIVER SUBARNAREKHA AT GOPIBALLAVPUR (WEST BENGAL)WEST BENGAL25.035.06.812.57.08.2153.0393.01.01.00.320.37110.002200000.01400.035000000.02.078.0
160230069RIVER SUBARNAREKHA AT LAKHANNATH (ORISSA)ODISHA24.031.07.29.27.08.5152.0334.01.01.00.320.32170.001100000.01700.028000000.02.078.0
16034086RIVER SUBARNAREKHA U/S HINDALCO IND LTD, MURLI WORKS, CHHOTAMURI, RANCHIJHARKHAND15.027.07.47.96.77.42.02.80.00.0NaNNaNNaNNaNNaNNaNNaNNaN
16044756RIVER SUBARNAREKHA AT RUCCA DAM, RUCCA, RANCHIJHARKHAND8.623.07.68.07.27.31.73.10.00.0NaNNaNNaNNaNNaNNaNNaNNaN
16052423RIVER BUDHABALANGA AT D/S OF BARIPADA TOWNODISHA20.030.05.29.27.28.4146.0326.01.32.20.000.87780.004900.02800.017000.05.049.0
16063943RIVER BUDHABALANGA AT BALASORE U/SODISHA21.030.05.69.66.98.5125.0383.01.12.40.320.86230.002300.01300.04900.0NaNNaN
16073942RIVER SONO AT KANAKDURGA ROAD NEAR REMUNA, HATOGONDODISHA20.030.03.29.66.98.5120.0433.01.11.80.320.9378.0013000.0490.035000.0NaNNaN
1608211520.0 22.0 6.6 7.7 7.7 8.6 202 572 1.0 1.8 0.57 1.42 2 2 37 63NaN2115.020.022.06.67.77.78.6202.0572.01.01.800.571.422.02.037.063.0NaN
16094753RIVER HARMU NEAR HARMU BRIDGE, HARMU, RANCHIJHARKHAND11.024.02.93.66.56.62.511.70.00.0NaNNaNNaNNaNNaNNaNNaNNaN
16104754RIVER HARMU BEFORE METTING TO SWARNREKHA RIVERJHARKHAND11.425.03.14.86.56.74.511.10.00.0NaNNaNNaNNaNNaNNaNNaNNaN